scholarly journals bletl - A Python package for integrating microbioreactors in the design-build-test-learn cycle

2021 ◽  
Author(s):  
Michael Osthege ◽  
Niklas Tenhaef ◽  
Rebecca Zyla ◽  
Carolin Mueller ◽  
Johannes Hemmerich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Time Variable ◽  
Machine Learning Techniques ◽  
Calibration Model ◽  
Process Data ◽  
Learning Techniques ◽  
Interactive Visualizations ◽  
Short Time ◽  
Python Package

Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rate μ(t) based on a novel method of unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switchpoints that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, t-distributed Stochastic Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes.

scholarly journals Analysis of Teeline Shorthand Recognition using Machine Learning and Deep Learning Techniques.

2020 ◽  
Vol 9 (4) ◽  
pp. 2133-2138
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Recognition System ◽  
Machine Learning Techniques ◽  
Handheld Devices ◽  
Original Text ◽  
Language Recognition ◽  
Learning Techniques ◽  
Learning Technique ◽  
Short Time

In order to take notes of the speech delivered by the VIPs in the short time short hand language is employed. Mainly there are two shorthand languages namely Pitman and Teeline. An automatic shorthand language recognition system is essential in order to make use of the handheld devices for speedy conversion to the original text. The paper addresses and compares the recognition of the Teeline alphabets using the Machine learning (SVM and KNN) and deep learning (CNN) techniques. The dataset has been prepared using the digital pen and the same is processed and stored using the android application. The prepared dataset is fed to the proposed system and accuracy of recognition is compared. Deep learning technique gave higher accuracy compared to machine learning techniques. MATLAB 2018b platform is used for implementation of the experimental setup.

scholarly journals Simulation of a CSP Solar Steam Generator, Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3613
Author(s):  
Adrian Gonzalez Gonzalez ◽  
Jose Valeriano Alvarez Cabal ◽  
Miguel Angel Vigil Berrocal ◽  
Rogelio Peón Menéndez ◽  
Adrian Riesgo Fernández
Keyword(s):  
Machine Learning ◽  
Performance Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Concentrated Solar Power ◽  
Process Data ◽  
Mena Region ◽  
Power Block ◽  
Learning Techniques ◽  
Fitting In

Developing an accurate concentrated solar power (CSP) performance model requires significant effort and time. The power block (PB) is the most complex system, and its modeling is clearly the most complicated and time-demanding part. Nonetheless, PB layouts are quite similar throughout CSP plants, meaning that there are enough historical process data available from commercial plants to use machine learning techniques. These algorithms allowed the development of a very accurate black-box PB model in a very short amount of time. This PB model could be easily integrated as a block into the PM. The machine learning technique selected was SVR (support vector regression). The PB model was trained using a complete year of data from a commercial CSP plant situated in southern Spain. With a very limited set of inputs, the PB model results were very accurate, according to their validation against a new complete year of data. The model not only fit well on an aggregate basis, but also in the transients between operation modes. To validate applicability, the same model methodology is used with a data from a very different CSP Plant, located in the MENA region and with more than double nominal electric power, obtaining an excellent fitting in the validation.

scholarly journals Predicting Growth Trajectory in Vestibular Schwannoma From Radiomic Data Using Supervised Machine Learning Techniques

Neurosurgery ◽  
2019 ◽  
Vol 66 (Supplement_1) ◽  
Author(s):  
Conor Grady ◽  
Hesheng Wang ◽  
Zane Schnurman ◽  
Tanxia Qu ◽  
Douglas Kondziolka
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Vestibular Schwannoma ◽  
High Growth ◽  
High Growth Rate ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Volumetric Growth ◽  
Learning Techniques ◽  
Pre Treatment

Abstract INTRODUCTION Clinicians are not able to predict the growth-rate of a vestibular schwannoma (VS) by reviewing a standard MRI. Recently, the field of radiomics has enabled high-dimensional, quantitative datasets to be created from imaging obtained during routine clinical care. This study investigates whether supervised machine learning techniques can yield accurate predictions of volumetric growth-rate based on radiomic data from MRIs of treatment-naïve VSs. METHODS A total of 212 patients diagnosed with unilateral VS between 2012 and 2018 underwent measurement of tumor volume on all pre-treatment MRIs. The number of MRIs per patient ranged from 2 to 11, totaling 699 individual studies. Annualized volumetric growth-rate was calculated for each patient. Two cohorts were formed from the 30 patients with the lowest (−20% to 10%) and the 40 patients with the highest (55%–165%) annualized growth-rates, respectively. Manual segmentation of tumor volumes on the last pre-treatment MRI for each patient was performed using 3D Slicer. Pyradiomics was used to calculate histogram, shape, and texture parameters from ADC, CISS, T2 weighted, and postcontrast T1 weighted sequences, resulting in a total of 311 radiomic parameters per volume of interest. Two models predicting cohort membership, a random forest classifier (RFC) and a gradient boosted trees (XGBoost) algorithm, were then trained on a training dataset containing the radiomic profiles of 25 patients from the low growth-rate cohort and 35 patients from the high growth-rate cohort. The models were then tested against the radiomic profiles of the 10 patients withheld from the training group. RESULTS Following tuning of hyperparameters, both models were able to predict individual tumor membership in the low-growth-rate or high growth-rate cohorts with 100% accuracy. Area under the receiver operating curve (ROC) curve (AUC) was 1.0 for both models. CONCLUSION Supervised machine learning techniques can predict growth-rate in VS based on radiomic parameters. External validation is warranted.

scholarly journals Heart Disease Prediction Based on an Optimal Feature Selection Method using Autoencoder

2020 ◽  
pp. 25-38
Author(s):  
Azhar M. A. ◽  
Princy Ann Thomas
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Classification Problems ◽  
Process Data ◽  
Integration Algorithm ◽  
Learning Techniques ◽  
Hybrid Classification

Heart Failure is one of the common diseases that can lead to dangerous situations. There are several data available within the healthcare systems. However, there was an absence of successful analysis methods to find connections and patterns in health care data. Some Machine learning methods can help us remedy this circumstance. This helps in getting a better insight into the concept of a classification problem. In many classification problems, it is difficult to learn good classifiers before removing these unwanted features due to the huge size of the data. In my work, we have used an artificial neural network-based autoencoder for effective feature selection The aim of feature selection is improving prediction performance and providing a better understanding of the process data. Hybrid Classification method with a dynamic integration algorithm for classification that aims at finding optimal features by applying machine learning techniques resulting in improving the performance in the prediction of cardiovascular disease.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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